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A stochastic framework for predicting epidemiological risk areas using the Ornstein-Uhlenbeck process: Software and supplementary material

Gonçalves, Glênio1; Buske, Daniela1; Quadros, Régis1; Bruhn, Fábio1; Tumelero, Fernanda2; Faria, Aléxia1; Gomes, Angelita1

Research facility: Universidade Federal de Pelotas
Published Nov 05, 2024; Updated Jan 16, 2025 on Dryad. https://doi.org/10.5061/dryad.hmgqnk9rr

Data files

Nov 05, 2024 version files 6.38 KB
Jan 16, 2025 version files 7.54 KB

Abstract

Understanding the spatiotemporal distribution of infection risk is fundamental in the epidemiology of infectious diseases, as it allows for the identification of key parameters influencing disease transmission. Insights into the spatiotemporal drivers of epidemic dynamics are essential for developing improved strategies for disease prevention. This study introduces a predictive framework based on the Ornstein-Uhlenbeck stochastic process to estimate the spatial and temporal distribution of infectiousness originating from a primary case. The proposed model captures the dynamics of secondary infections and their impact on spatial dispersion, primarily driven by a diffusion mechanism of the Chapman type. This diffusion mechanism induces the phenomenon of segregation by incorporating behavioral or cognitive aspects of susceptible individuals. We calculate critical epidemiological metrics, including the basic reproduction number, the probability density function of generation time, and the mean generation time. Notably, the model reveals that 38.5% of dengue infections occur before the onset of symptoms, highlighting the critical need to address presymptomatic transmission in control strategies. This silent dissemination increases the complexity of the objective of the model presented, which seeks to answer the fundamental public health question of when the pathogen will reach a specific region. The proposed mathematical model establishes a framework for selecting emerging risk areas, prioritizing interventions and optimizing resource allocation.